Skip to content

Bump the pip group across 1 directory with 10 updates #10

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom
Open
dependabot wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Bump the pip group across 1 directory with 10 updates #10

dependabot wants to merge 1 commit into from

Conversation

@dependabot
Copy link

@dependabot dependabot bot commented on behalf of github Jan 8, 2025

Bumps the pip group with 10 updates in the /manual-testing-sandbox directory:

Package From To
flask 2.0.2 2.2.5
requests 2.26.0 2.32.2
scikit-learn 0.24.2 1.5.0
tensorflow 2.6.0 2.12.1
nltk 3.6.3 3.9
torch 1.9.1 2.2.0
keras 2.6.0 3.8.0
pillow 8.3.2 10.3.0
opencv-python 4.5.3.56 4.8.1.78
gunicorn 20.1.0 22.0.0

Updates flask from 2.0.2 to 2.2.5

Release notes

Sourced from flask's releases.

2.2.5

This is a security fix release for the 2.2.x release branch. Note that 2.3.x is the currently supported release branch; please upgrade to the latest version if possible.

2.2.4

This is a fix release for the 2.2.x release branch.

2.2.3

This is a fix release for the 2.2.x release branch.

2.2.2

This is a fix release for the 2.2.0 feature release.

2.2.1

This is a fix release for the 2.2.0 feature release.

2.2.0

This is a feature release, which includes new features and removes previously deprecated code. The 2.2.x branch is now the supported bug fix branch, the 2.1.x branch will become a tag marking the end of support for that branch. We encourage everyone to upgrade, and to use a tool such as pip-tools to pin all dependencies and control upgrades.

2.1.3

2.1.2

This is a fix release for the 2.1.0 feature release.

2.1.1

This is a fix release for the 2.1.0 feature release.

... (truncated)

Changelog

Sourced from flask's changelog.

Version 2.2.5

Released 2023-05-02

  • Update for compatibility with Werkzeug 2.3.3.
  • Set Vary: Cookie header when the session is accessed, modified, or refreshed.

Version 2.2.4

Released 2023-04-25

  • Update for compatibility with Werkzeug 2.3.

Version 2.2.3

Released 2023-02-15

  • Autoescape is enabled by default for .svg template files. :issue:4831
  • Fix the type of template_folder to accept pathlib.Path. :issue:4892
  • Add --debug option to the flask run command. :issue:4777

Version 2.2.2

Released 2022-08-08

  • Update Werkzeug dependency to >= 2.2.2. This includes fixes related to the new faster router, header parsing, and the development server. :pr:4754
  • Fix the default value for app.env to be "production". This attribute remains deprecated. :issue:4740

Version 2.2.1

Released 2022-08-03

  • Setting or accessing json_encoder or json_decoder raises a deprecation warning. :issue:4732

Version 2.2.0

... (truncated)

Commits

Updates requests from 2.26.0 to 2.32.2

Release notes

Sourced from requests's releases.

v2.32.2

2.32.2 (2024-05-21)

Deprecations

  • To provide a more stable migration for custom HTTPAdapters impacted by the CVE changes in 2.32.0, we've renamed _get_connection to a new public API, get_connection_with_tls_context. Existing custom HTTPAdapters will need to migrate their code to use this new API. get_connection is considered deprecated in all versions of Requests>=2.32.0.

    A minimal (2-line) example has been provided in the linked PR to ease migration, but we strongly urge users to evaluate if their custom adapter is subject to the same issue described in CVE-2024-35195. (#6710)

v2.32.1

2.32.1 (2024-05-20)

Bugfixes

  • Add missing test certs to the sdist distributed on PyPI.

v2.32.0

2.32.0 (2024-05-20)

🐍 PYCON US 2024 EDITION 🐍

Security

  • Fixed an issue where setting verify=False on the first request from a Session will cause subsequent requests to the same origin to also ignore cert verification, regardless of the value of verify. (GHSA-9wx4-h78v-vm56)

Improvements

  • verify=True now reuses a global SSLContext which should improve request time variance between first and subsequent requests. It should also minimize certificate load time on Windows systems when using a Python version built with OpenSSL 3.x. (#6667)
  • Requests now supports optional use of character detection (chardet or charset_normalizer) when repackaged or vendored. This enables pip and other projects to minimize their vendoring surface area. The Response.text() and apparent_encoding APIs will default to utf-8 if neither library is present. (#6702)

Bugfixes

  • Fixed bug in length detection where emoji length was incorrectly calculated in the request content-length. (#6589)
  • Fixed deserialization bug in JSONDecodeError. (#6629)
  • Fixed bug where an extra leading / (path separator) could lead urllib3 to unnecessarily reparse the request URI. (#6644)

... (truncated)

Changelog

Sourced from requests's changelog.

2.32.2 (2024-05-21)

Deprecations

  • To provide a more stable migration for custom HTTPAdapters impacted by the CVE changes in 2.32.0, we've renamed _get_connection to a new public API, get_connection_with_tls_context. Existing custom HTTPAdapters will need to migrate their code to use this new API. get_connection is considered deprecated in all versions of Requests>=2.32.0.

    A minimal (2-line) example has been provided in the linked PR to ease migration, but we strongly urge users to evaluate if their custom adapter is subject to the same issue described in CVE-2024-35195. (#6710)

2.32.1 (2024-05-20)

Bugfixes

  • Add missing test certs to the sdist distributed on PyPI.

2.32.0 (2024-05-20)

Security

  • Fixed an issue where setting verify=False on the first request from a Session will cause subsequent requests to the same origin to also ignore cert verification, regardless of the value of verify. (GHSA-9wx4-h78v-vm56)

Improvements

  • verify=True now reuses a global SSLContext which should improve request time variance between first and subsequent requests. It should also minimize certificate load time on Windows systems when using a Python version built with OpenSSL 3.x. (#6667)
  • Requests now supports optional use of character detection (chardet or charset_normalizer) when repackaged or vendored. This enables pip and other projects to minimize their vendoring surface area. The Response.text() and apparent_encoding APIs will default to utf-8 if neither library is present. (#6702)

Bugfixes

  • Fixed bug in length detection where emoji length was incorrectly calculated in the request content-length. (#6589)
  • Fixed deserialization bug in JSONDecodeError. (#6629)
  • Fixed bug where an extra leading / (path separator) could lead urllib3 to unnecessarily reparse the request URI. (#6644)

Deprecations

... (truncated)

Commits
  • 88dce9d v2.32.2
  • c98e4d1 Merge pull request #6710 from nateprewitt/api_rename
  • 92075b3 Add deprecation warning
  • aa1461b Move _get_connection to get_connection_with_tls_context
  • 970e8ce v2.32.1
  • d6ebc4a v2.32.0
  • 9a40d12 Avoid reloading root certificates to improve concurrent performance (#6667)
  • 0c030f7 Merge pull request #6702 from nateprewitt/no_char_detection
  • 555b870 Allow character detection dependencies to be optional in post-packaging steps
  • d6dded3 Merge pull request #6700 from franekmagiera/update-redirect-to-invalid-uri-test
  • Additional commits viewable in compare view

Updates scikit-learn from 0.24.2 to 1.5.0

Release notes

Sourced from scikit-learn's releases.

Scikit-learn 1.5.0

We're happy to announce the 1.5.0 release.

You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_5_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.5.html

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn

Scikit-learn 1.4.2

We're happy to announce the 1.4.2 release.

This release only includes support for numpy 2.

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:

pip install -U scikit-learn

Scikit-learn 1.4.1.post1

We're happy to announce the 1.4.1.post1 release.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.4.html#version-1-4-1-post1

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn

... (truncated)

Commits

Updates tensorflow from 2.6.0 to 2.12.1

Release notes

Sourced from tensorflow's releases.

TensorFlow 2.12.1

Release 2.12.1

Bug Fixes and Other Changes

  • The use of the ambe config to build and test aarch64 is not needed. The ambe config will be removed in the future. Making cpu_arm64_pip.sh and cpu_arm64_nonpip.sh more similar for easier future maintenance.

TensorFlow 2.12.0

Release 2.12.0

TensorFlow

Breaking Changes

  • Build, Compilation and Packaging

    • Removed redundant packages tensorflow-gpu and tf-nightly-gpu. These packages were removed and replaced with packages that direct users to switch to tensorflow or tf-nightly respectively. Since TensorFlow 2.1, the only difference between these two sets of packages was their names, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorflow-gpu for more details.

  • tf.function:

    • tf.function now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on. This change may break code where the function signature is malformed, but was ignored previously, such as:
      • Using functools.wraps on a function with different signature
      • Using functools.partial with an invalid tf.function input
    • tf.function now enforces input parameter names to be valid Python identifiers. Incompatible names are automatically sanitized similarly to existing SavedModel signature behavior.
    • Parameterless tf.functions are assumed to have an empty input_signature instead of an undefined one even if the input_signature is unspecified.
    • tf.types.experimental.TraceType now requires an additional placeholder_value method to be defined.
    • tf.function now traces with placeholder values generated by TraceType instead of the value itself.

  • Experimental APIs tf.config.experimental.enable_mlir_graph_optimization and tf.config.experimental.disable_mlir_graph_optimization were removed.

Major Features and Improvements

  • Support for Python 3.11 has been added.

  • Support for Python 3.7 has been removed. We are not releasing any more patches for Python 3.7.

  • tf.lite:

    • Add 16-bit float type support for built-in op fill.
    • Transpose now supports 6D tensors.
    • Float LSTM now supports diagonal recurrent tensors: https://arxiv.org/abs/1903.08023

  • tf.experimental.dtensor:

    • Coordination service now works with dtensor.initialize_accelerator_system, and enabled by default.
    • Add tf.experimental.dtensor.is_dtensor to check if a tensor is a DTensor instance.

  • tf.data:

    • Added support for alternative checkpointing protocol which makes it possible to checkpoint the state of the input pipeline without having to store the contents of internal buffers. The new functionality can be enabled through the experimental_symbolic_checkpoint option of tf.data.Options().
    • Added a new rerandomize_each_iteration argument for the tf.data.Dataset.random() operation, which controls whether the sequence of generated random numbers should be re-randomized every epoch or not (the default behavior). If seed is set and rerandomize_each_iteration=True, the random() operation will produce a different (deterministic) sequence of numbers every epoch.

... (truncated)

Changelog

Sourced from tensorflow's changelog.

Release 2.12.1

Bug Fixes and Other Changes

  • The use of the ambe config to build and test aarch64 is not needed. The ambe config will be removed in the future. Making cpu_arm64_pip.sh and cpu_arm64_nonpip.sh more similar for easier future maintenance.

Release 2.12.0

Breaking Changes

  • Build, Compilation and Packaging

    • Removed redundant packages tensorflow-gpu and tf-nightly-gpu. These packages were removed and replaced with packages that direct users to switch to tensorflow or tf-nightly respectively. Since TensorFlow 2.1, the only difference between these two sets of packages was their names, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorflow-gpu for more details.

  • tf.function:

    • tf.function now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on. This change may break code where the function signature is malformed, but was ignored previously, such as:
      • Using functools.wraps on a function with different signature
      • Using functools.partial with an invalid tf.function input
    • tf.function now enforces input parameter names to be valid Python identifiers. Incompatible names are automatically sanitized similarly to existing SavedModel signature behavior.
    • Parameterless tf.functions are assumed to have an empty input_signature instead of an undefined one even if the input_signature is unspecified.
    • tf.types.experimental.TraceType now requires an additional placeholder_value method to be defined.
    • tf.function now traces with placeholder values generated by TraceType instead of the value itself.

  • Experimental APIs tf.config.experimental.enable_mlir_graph_optimization and tf.config.experimental.disable_mlir_graph_optimization were removed.

Major Features and Improvements

  • Support for Python 3.11 has been added.

  • Support for Python 3.7 has been removed. We are not releasing any more patches for Python 3.7.

  • tf.lite:

    • Add 16-bit float type support for built-in op fill.
    • Transpose now supports 6D tensors.
    • Float LSTM now supports diagonal recurrent tensors: https://arxiv.org/abs/1903.08023

  • tf.experimental.dtensor:

    • Coordination service now works with dtensor.initialize_accelerator_system, and enabled by default.
    • Add tf.experimental.dtensor.is_dtensor to check if a tensor is a DTensor instance.

  • tf.data:

    • Added support for alternative checkpointing protocol which makes it possible to checkpoint the state of the input pipeline without having to store the contents of internal buffers. The new functionality can be enabled through the experimental_symbolic_checkpoint option of tf.data.Options().
    • Added a new rerandomize_each_iteration argument for the tf.data.Dataset.random() operation, which controls whether the sequence of generated random numbers should be re-randomized every epoch or not (the default behavior). If seed is set and rerandomize_each_iteration=True, the random() operation will produce a different (deterministic) sequence of numbers every epoch.
    • Added a new rerandomize_each_iteration argument for the tf.data.Dataset.sample_from_datasets() operation, which controls whether the sequence of generated random numbers used for sampling should be re-randomized every epoch or not. If seed is set and rerandomize_each_iteration=True, the sample_from_datasets() operation will use a different (deterministic) sequence of numbers every epoch.

  • tf.test:

... (truncated)

Commits
  • 8e2b665 Merge pull request #61094 from tensorflow/venkat-patch-444
  • 02478f0 Fix unit test failure caused by numpy update
  • 2cd9b41 Merge pull request #61082 from tensorflow/venkat-patch-333
  • 7995c95 Updating Simplified retry logic to DNS cache
  • 29479ed Merge pull request #60872 from tensorflow/r2.12-c45a6c0b1cb
  • e76a933 Simplified retry logic to DNS cache
  • 76addf7 Merge pull request #60850 from elfringham/non_pip_fix
  • 05987a8 [Linaro:ARM_CI] Fix permissions for running nonpip tests
  • 23724d2 Merge pull request #60842 from elfringham/r2.12
  • 496730b Limit typing_extensions to less than 4.6.0 until it works
  • Additional commits viewable in compare view

Updates nltk from 3.6.3 to 3.9

Changelog

Sourced from nltk's changelog.

Version 3.9.1 2024-08-19

  • Fixed bug that prevented wordnet from loading

Version 3.9 2024-08-18

  • Fix security vulnerability CVE-2024-39705 (breaking change)
  • Replace pickled models (punkt, chunker, taggers) by new pickle-free "_tab" packages
  • No longer sort Wordnet synsets and relations (sort in calling function when required)
  • Only strip the last suffix in Wordnet Morphy, thus restricting synsets() results
  • Add Python 3.12 support
  • Many other minor fixes

Thanks to the following contributors to 3.8.2: Tom Aarsen, Cat Lee Ball, Veralara Bernhard, Carlos Brandt, Konstantin Chernyshev, Michael Higgins, Eric Kafe, Vivek Kalyan, David Lukes, Rob Malouf, purificant, Alex Rudnick, Liling Tan, Akihiro Yamazaki.

Version 3.8.1 2023-01-02

  • Resolve RCE vulnerability in localhost WordNet Browser (#3100)
  • Remove unused tool scripts (#3099)
  • Resolve XSS vulnerability in localhost WordNet Browser (#3096)
  • Add Python 3.11 support (#3090)

Thanks to the following contributors to 3.8.1: Francis Bond, John Vandenberg, Tom Aarsen

Version 3.8 2022-12-12

  • Refactor dispersion plot (#3082)
  • Provide type hints for LazyCorpusLoader variables (#3081)
  • Throw warning when LanguageModel is initialized with incorrect vocabulary (#3080)
  • Fix WordNet's all_synsets() function (#3078)
  • Resolve TreebankWordDetokenizer inconsistency with end-of-string contractions (#3070)
  • Support both iso639-3 codes and BCP-47 language tags (#3060)
  • Avoid DeprecationWarning in Regexp tokenizer (#3055)
  • Fix many doctests, add doctests to CI (#3054, #3050, #3048)
  • Fix bool field not being read in VerbNet (#3044)
  • Greatly improve time efficiency of SyllableTokenizer when tokenizing numbers (#3042)
  • Fix encodings of Polish udhr corpus reader (#3038)
  • Allow TweetTokenizer to tokenize emoji flag sequences (#3034)
  • Prevent LazyModule from increasing the size of nltk.dict (#3033)
  • Fix CoreNLPServer non-default port issue (#3031)
  • Add "acion" suffix to the Spanish SnowballStemmer (#3030)
  • Allow loading WordNet without OMW (#3026)
  • Use input() in nltk.chat.chatbot() for Jupyter support (#3022)
  • Fix edit_distance_align() in distance.py (#3017)
  • Tackle performance and accuracy regression of sentence tokenizer since NLTK 3.6.6 (#3014)
  • Add the Iota operator to semantic logic (#3010)
  • Resolve critical errors in WordNet app (#3008)
  • Resolve critical error in CHILDES Corpus (#2998)
  • Make WordNet information_content() accept adjective satellites (#2995)

... (truncated)

Commits

Updates torch from 1.9.1 to 2.2.0

Release notes

Sourced from torch's releases.

PyTorch 2.2: FlashAttention-v2, AOTInductor

PyTorch 2.2 Release Notes

  • Highlights
  • Backwards Incompatible Changes
  • Deprecations
  • New Features
  • Improvements
  • Bug fixes
  • Performance
  • Documentation

Highlights

We are excited to announce the release of PyTorch® 2.2! PyTorch 2.2 offers ~2x performance improvements to scaled_dot_product_attention via FlashAttention-v2 integration, as well as AOTInductor, a new ahead-of-time compilation and deployment tool built for non-python server-side deployments.

This release also includes improved torch.compile support for Optimizers, a number of new inductor optimizations, and a new logging mechanism called TORCH_LOGS.

Please note that we are deprecating macOS x86 support, and PyTorch 2.2.x will be the last version that supports macOS x64.

Along with 2.2, we are also releasing a series of updates to the PyTorch domain libraries. More details can be found in the library updates blog.

This release is composed of 3,628 commits and 521 contributors since PyTorch 2.1. We want to sincerely thank our dedicated community for your contributions. As always, we encourage you to try these out and report any issues as we improve 2.2. More information about how to get started with the PyTorch 2-series can be found at our Getting Started page.

Summary:

  • scaled_dot_product_attention (SDPA) now supports FlashAttention-2, yielding around 2x speedups compared to previous versions.
  • PyTorch 2.2 introduces a new ahead-of-time extension of TorchInductor called AOTInductor, designed to compile and deploy PyTorch programs for non-python server-side.
  • torch.distributed supports a new abstraction for initializing and representing ProcessGroups called device_mesh.
  • PyTorch 2.2 ships a standardized, configurable logging mechanism called TORCH_LOGS.
  • A number of torch.compile improvements are included in PyTorch 2.2, including improved support for compiling Optimizers and improved TorchInductor fusion and layout optimizations.
  • Please note that we are deprecating macOS x86 support, and PyTorch 2.2.x will be the last version that supports macOS x64.
  • torch.ao.quantization now offers a prototype torch.export based flow

... (truncated)

Changelog

Sourced from torch's changelog.

Releasing PyTorch

Release Compatibility Matrix

Following is the Release Compatibility Matrix for PyTorch releases:

... (truncated)

Commits

Updates keras from 2.6.0 to 3.8.0

Release notes

Sourced from keras's releases.

Keras 3.8.0

New: OpenVINO backend

OpenVINO is now available as an infererence-only Keras backend. You can start using it by setting the backend field to "open_vino" in your keras.json config file.

OpenVINO is a deep learning inference-only framework tailored for CPU (x86, ARM), certain GPUs (OpenCL capable, integrated and discrete) and certain AI accelerators (Intel NPU).

Because OpenVINO does not support gradients, you cannot use it for training (e.g. model.fit()) -- only inference. You can train your models with the JAX/TensorFlow/PyTorch backends, and when trained, reload them with the OpenVINO backend for inference on a target device supported by OpenVINO.

New: ONNX model export

You can now export your Keras models to the ONNX format from the JAX, TensorFlow, and PyTorch backends.

Just pass format="onnx" in your model.export() call:

# Export the model as a ONNX artifact
model.export("path/to/location", format="onnx")
Load the artifact in a different process/environment
ort_session = onnxruntime.InferenceSession("path/to/location")
Run inference
ort_inputs = {
k.name: v for k, v in zip(ort_session.get_inputs(), input_data)
}
predictions = ort_session.run(None, ort_inputs)

New: Scikit-Learn API compatibility interface

It's now possible to easily integrate Keras models into Sciki-Learn pipelines! The following wrapper classes are available:

  • keras.wrappers.SKLearnClassifier: implements the sklearn Classifier API
  • keras.wrappers.SKLearnRegressor: implements the sklearn Regressor API
  • keras.wrappers.SKLearnTransformer: implements the sklearn Transformer API

Other feature additions

  • Add new ops:
    • Add keras.ops.diagflat
    • Add keras.ops.unravel_index
  • Add new activations:
    • Add sparse_plus activation
    • Add sparsemax activation
  • Add new image augmentation and preprocessing layers:
    • Add keras.layers.RandAugment
    • Add keras.layers.Equalization
    • Add keras.layers.MixUp

... (truncated)

Commits

Updates pillow from 8.3.2 to 10.3.0

Release notes

Sourced from pillow's releases.

10.3.0

https://pillow.readthedocs.io/en/stable/releasenotes/10.3.0.html

Deprecations

  • Deprecate eval(), replacing it with lambda_eval() and unsafe_eval() #7927 [@​hugovk]
  • Deprecate ImageCms constants and versions() function #7702 [@​nulano]

Changes

@dependabot
Bump the pip group across 1 directory with 10 updates
28473d0 
Bumps the pip group with 10 updates in the /manual-testing-sandbox directory:

| Package | From | To |
| --- | --- | --- |
| [flask](https://github.com/pallets/flask) | `2.0.2` | `2.2.5` |
| [requests](https://github.com/psf/requests) | `2.26.0` | `2.32.2` |
| [scikit-learn](https://github.com/scikit-learn/scikit-learn) | `0.24.2` | `1.5.0` |
| [tensorflow](https://github.com/tensorflow/tensorflow) | `2.6.0` | `2.12.1` |
| [nltk](https://github.com/nltk/nltk) | `3.6.3` | `3.9` |
| [torch](https://github.com/pytorch/pytorch) | `1.9.1` | `2.2.0` |
| [keras](https://github.com/keras-team/keras) | `2.6.0` | `3.8.0` |
| [pillow](https://github.com/python-pillow/Pillow) | `8.3.2` | `10.3.0` |
| [opencv-python](https://github.com/opencv/opencv-python) | `4.5.3.56` | `4.8.1.78` |
| [gunicorn](https://github.com/benoitc/gunicorn) | `20.1.0` | `22.0.0` |



Updates `flask` from 2.0.2 to 2.2.5
- [Release notes](https://github.com/pallets/flask/releases)
- [Changelog](https://github.com/pallets/flask/blob/main/CHANGES.rst)
- [Commits](pallets/flask@2.0.2...2.2.5)

Updates `requests` from 2.26.0 to 2.32.2
- [Release notes](https://github.com/psf/requests/releases)
- [Changelog](https://github.com/psf/requests/blob/main/HISTORY.md)
- [Commits](psf/requests@v2.26.0...v2.32.2)

Updates `scikit-learn` from 0.24.2 to 1.5.0
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](scikit-learn/scikit-learn@0.24.2...1.5.0)

Updates `tensorflow` from 2.6.0 to 2.12.1
- [Release notes](https://github.com/tensorflow/tensorflow/releases)
- [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md)
- [Commits](tensorflow/tensorflow@v2.6.0...v2.12.1)

Updates `nltk` from 3.6.3 to 3.9
- [Changelog](https://github.com/nltk/nltk/blob/develop/ChangeLog)
- [Commits](nltk/nltk@3.6.3...3.9)

Updates `torch` from 1.9.1 to 2.2.0
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v1.9.1...v2.2.0)

Updates `keras` from 2.6.0 to 3.8.0
- [Release notes](https://github.com/keras-team/keras/releases)
- [Commits](keras-team/keras@v2.6.0...v3.8.0)

Updates `pillow` from 8.3.2 to 10.3.0
- [Release notes](https://github.com/python-pillow/Pillow/releases)
- [Changelog](https://github.com/python-pillow/Pillow/blob/main/CHANGES.rst)
- [Commits](python-pillow/Pillow@8.3.2...10.3.0)

Updates `opencv-python` from 4.5.3.56 to 4.8.1.78
- [Release notes](https://github.com/opencv/opencv-python/releases)
- [Commits](https://github.com/opencv/opencv-python/commits)

Updates `gunicorn` from 20.1.0 to 22.0.0
- [Release notes](https://github.com/benoitc/gunicorn/releases)
- [Commits](benoitc/gunicorn@20.1.0...22.0.0)

---
updated-dependencies:
- dependency-name: flask
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: requests
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: scikit-learn
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: tensorflow
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: nltk
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: torch
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: keras
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: pillow
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: opencv-python
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: gunicorn
  dependency-type: direct:production
  dependency-group: pip
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot dependabot bot added dependencies Pull requests that update a dependency file python Pull requests that update Python code labels Jan 8, 2025
@dependabot dependabot bot mentioned this pull request Jan 8, 2025
Closed
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Reviewers

No reviews

Assignees

No one assigned

Labels

dependencies Pull requests that update a dependency file python Pull requests that update Python code

Projects

None yet

Milestone

No milestone

Development

Successfully merging this pull request may close these issues.

1 participant